Bibliographic record
Abstract
In theory, a meteor shower can be distinguished from the sporadic meteor background by its short duration and orbital similarity. In practice, the duration and strength of a shower and the orbital similarity between its constituent meteors varies widely between showers. Further complicating matters is the anisotropy of the sporadic background. These combined factors make it difficult to distinguish between shower and sporadic meteors with a single, static set of criteria. The orbital similarity, or D-, parameters are often used to assess the relationship between meteors [1,2,3]. The more dissimilar two orbits are, the higher their computed D value will be; generally, meteors are considered related if their D-parameter falls below some cutoff value [4]. However, this approach will include some sporadic meteors, and when a weak shower lies near a sporadic source, the false positive rate for shower association can be quite high. Additionally, this cutoff approach does not assess whether the shower itself is significant. We present a method for using D-parameters to extract showers from a dataset that automatically takes shower strength into account and tests for significance [5]. We accomplish this by calculating the false positive rate for shower association using "shower analogs," which are identical to the original shower except in solar longitude. This method is applied to a set of more than 30,000 meteors detected by the NASA All-Sky Fireball Network [6] and the Southern Ontario Meteor Network (SOMN) [7]. We previously detected 29 showers in our data using this method [5]; now, with another year of data, we have several additional detections. Figure 1 presents one example: the 2016 July gamma Draconid outburst. There are several benefits to using our method. First, it provides a test of shower significance (see Fig. 2 for an example of a non-detection). Second, it quantifies the probability that a meteor belongs to a given shower as a function of D-parameter. Finally, it quantifies the strength of a shower, even when individual members cannot be identified with 100% accuracy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.019 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".